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But, could this "model" be used for something like monitoring land use in a city? The specific example I'm thinking of is getting a percentage breakdown of what land devoted to paved surfaces (parking/roads), to vacant undeveloped lots, and to built structures. It would also be interesting to see how those percentages have changed over time.
Floods and fires are bad news, but how does this product actually get used to make things any different ?
Earlier quoted context omitted.
So what's the better word?
What it actually is: https://huggingface.co/ibm-nasa-geospatial/Prithvi-100M “Prithvi is a first-of-its-kind temporal Vision transformer” That is enormously more specific and interesting, and the word model is absent.
In contrast, if you’re publishing this in a machine learning journal, then sure—you should definitely call it a temporal Vision transformer.
This may be a silly question as I'm quite inexperienced w/ ML and a lot of the words in this article don't mean anything to me yet: But, could this "model" be used for something like monitoring land use in a city? The specific example I'm thinking of is getting a percentage breakdown of what land devoted to paved surfaces (parking/roads), to vacant undeveloped lots, and to built structures. It would also be interesti…
Unclear which LICENSE it is.
E.g. https://huggingface.co/ibm-nasa-geospatial/Prithvi-100M
(Though with all Facebook's shenanigans I understand the need to be sceptical and search for a license when a company claims "open source")
“Model” is a dangerously overloaded word in 2023, possibly worse than Object in 1996. When I worked on ML inference I would tease the researchers with the question “what is a model?” in the hope they would say something that constrained it in any way, but no, a model can be anything at all and is whatever you want it to be. As such this press release is perfect nonsense, which is kind of appropriate for a Watson offs…
This may be a silly question as I'm quite inexperienced w/ ML and a lot of the words in this article don't mean anything to me yet: But, could this "model" be used for something like monitoring land use in a city? The specific example I'm thinking of is getting a percentage breakdown of what land devoted to paved surfaces (parking/roads), to vacant undeveloped lots, and to built structures. It would also be interesti…
That may not be fine enough resolution in the source data to resolve parking lots Vs undeveloped areas to the degree you require.
The alternatives are to use the nature of the models (trained to lock in on multispectral signatures) on finer source data - which may limit your options about the globe, OR
to use urban data from city land agencies which have maps from low level air photo surveys with resolution down to 10 cm (+/-) and GIS land boundaries which are often classified via metadata.
Air photos are not multispectral (usually) so you won't have access to IR bands etc.
You can get coarse city growth figures globally from sat data going back to TERRA (launched 1999 IIRC) and fine grained air photo data from well off cities going back to (say) the mid 80s (and longer for wet negatives).
I'd be excited to see what a more substantial, state-of-the-art model could do with geospatial data.
Say, a model based on something like ViT-22B, with 22 billion parameters: https://arxiv.org/abs/2302.05442.
I suggest changing the link to https://huggingface.co/ibm-nasa-geospatial . The currently linked press release is an insufferable corporate PR word salad.